Body size trends in Neogene tortoises
30.05.2017
Test paleoTS with Fossil Checklist data (but is probably of no use, because they report average body sizes (means, median, something else? what are the respective sample size? maybe ask the authors!?), so this is just for playing around).
Raw data:
library(paleoTS)
#setwd("//naturkundemuseum-berlin.de/MuseumDFSRoot/Benutzer/Julia.Joos/Eigene Dateien/MA")
test<-read.csv("test26.5.csv", sep=";", header=TRUE)
test## Taxon Age_min Age_max Age_mean
## 1 Gopherus pertenuis 0.7810 1.8060 1.29350
## 2 Hesperotestudo johnstoni 0.7810 1.8060 1.29350
## 3 Hesperotestudo oelrichi 0.7810 1.8060 1.29350
## 4 Hesperotestudo turgida 0.7810 1.8060 1.29350
## 5 Megalochelys margae 0.7810 1.8060 1.29350
## 6 Megalochelys sondaari 0.7810 1.8060 1.29350
## 7 Megalochelys sp. [Flores] 0.7810 1.8060 1.29350
## 8 Megalochelys sp. [Java] 0.7810 1.8060 1.29350
## 9 Psammobates antiquorum 0.7810 1.8060 1.29350
## 10 Testudinidae sp. [China] 0.7810 1.8060 1.29350
## 11 Testudo changshanesis 0.7810 1.8060 1.29350
## 12 Hesperotestudo sp. [El Salvador] 0.7810 1.8060 1.29350
## 13 Aldabrachelys abrupta 0.0000 0.0117 0.00585
## 14 Aldabrachelys grandidieri 0.0000 0.0117 0.00585
## 15 Chelonoidis alburyorum 0.0000 0.0117 0.00585
## 16 Chelonoidis sp. [Caicos] 0.0000 0.0117 0.00585
## 17 Chelonoidis sp. [Turks] 0.0000 0.0117 0.00585
## 18 Titanochelon schafferi 5.3320 7.2460 6.28900
## 19 Chelonoidis elata 1.8060 7.2460 4.52600
## 20 Homopus fenestratus 3.6000 1.8060 2.70300
## 21 Chelonoidis lutzae 0.0117 0.1260 0.06885
## 22 Chelonoidis sombrerensis 0.0117 0.1260 0.06885
## 23 Chelonoidis sp. [Navassa] 0.0117 0.1260 0.06885
## 24 Gopherus donlaloi 0.0117 0.1260 0.06885
## 25 Hesperotestudo equicomes 0.0117 0.1260 0.06885
## 26 Hesperotestudo incisa 0.0117 0.1260 0.06885
## 27 Testudo suttoensis 0.0117 0.1260 0.06885
## 28 Hesperotestudo wilsoni 0.0010 0.1260 0.06350
## 29 Manouria oyamai 0.0010 0.1260 0.06350
## 30 Chelonoidis sp. [Hispaniola] 0.0010 0.1260 0.06350
## 31 Chelonoidis monensis 0.0000 0.1260 0.06300
## 32 Aldabrachelys laetoliensis 0.1260 3.6000 1.86300
## 33 Centrochelys marocana 0.1260 3.6000 1.86300
## 34 Gopherus sp. [Florida] 0.1260 3.6000 1.86300
## 35 Hesperotestudo campester 0.1260 3.6000 1.86300
## 36 Manouria punjabiensis 0.1260 3.6000 1.86300
## 37 Megalochelys atlas 0.1260 3.6000 1.86300
## 38 Megalochelys cautleyi 0.1260 3.6000 1.86300
## 39 Testudo or Agrionemys ranovi 0.1260 3.6000 1.86300
## 40 Testudo oughlamensis 0.1260 3.6000 1.86300
## 41 Testudo pecorinii 0.1260 3.6000 1.86300
## 42 Testudo transcaucasia 0.1260 3.6000 1.86300
## 43 Titanochelon sp. [Lesvos] 0.1260 3.6000 1.86300
## 44 Centrochelys vulcanica 0.1260 3.6000 1.86300
## 45 Centrochelys burchardi 0.1260 0.7810 0.45350
## 46 Centrochelys robusta 0.1260 0.7810 0.45350
## 47 Hesperotestudo bermudae 0.1260 0.7810 0.45350
## 48 Hesperotestudo mlynarskii 0.1260 0.7810 0.45350
## 49 Hesperotestudo percrassa 0.1260 0.7810 0.45350
## 50 Testudo kenitrensis 0.1260 0.7810 0.45350
## 51 Testudo lunellensis 0.1260 0.7810 0.45350
## 52 Titatochelon sp. [Ibiza] 0.1260 0.7810 0.45350
## 53 Hesperotestudo crassicutata 0.7810 0.0117 0.39635
## 54 Chelonoidis sp. [Curaçao] 0.0117 0.7810 0.39635
## 55 Gopherus laticaudatus 0.0117 0.7810 0.39635
## 56 Megalochelys sp. [Timor] 0.0117 0.7810 0.39635
## 57 Aldabrachelys gigantea daudinii 0.0000 0.0000 0.00000
## 58 Chelonoidis abingdonii 0.0000 0.0000 0.00000
## 59 Chelonoidis nigra 0.0000 0.0000 0.00000
## 60 Chelonoidis phantastica 0.0000 0.0000 0.00000
## 61 Chelonoidis sp. [Santa Fé] 0.0000 0.0000 0.00000
## 62 Chylindrapsis inepta 0.0000 0.0000 0.00000
## 63 Chylindrapsis peltastes 0.0000 0.0000 0.00000
## 64 Chylindrapsis triserrata 0.0000 0.0000 0.00000
## 65 Chylindraspis indica 0.0000 0.0000 0.00000
## 66 Chylindraspis vosmaeri 0.0000 0.0000 0.00000
## 67 Centrochelys atlantica 0.0117 2.5880 1.29985
## 68 Testudo sellovii 0.0117 2.5880 1.29985
## 69 Chelonoidis cubensis 0.1000 2.5880 1.34400
## 70 Titanochelon gymnesica 1.0000 3.6000 2.30000
## 71 Testudo kalganensis 1.0000 3.6000 2.30000
## Age CL_mean CL_range n
## 1 Early Pleistocene 107.5 1
## 2 Early Pleistocene 24.0 1
## 3 Early Pleistocene 28.0 1
## 4 Early Pleistocene 23.0 1
## 5 Early Pleistocene 165.0 1
## 6 Early Pleistocene 80.0 80-95 1
## 7 Early Pleistocene 120.0 180-200 1
## 8 Early Pleistocene 175.0 1
## 9 Early Pleistocene 11.0 60-65 1
## 10 Early Pleistocene 90.0 1
## 11 Early Pleistocene 33.0 1
## 12 Early to Late Pleistocene 150.0 1
## 13 Late Holocene 115.0 180-210 1
## 14 Late Holocene 125.0 1
## 15 Late Holocene 47.0 1
## 16 Late Holocene 75.0 1
## 17 Late Holocene 37.5 1
## 18 Late Miocene 192.5 90-100 1
## 19 Late Miocene to Early Pleistocene? 195.0 60-90 1
## 20 Late Neogene; possibly Pliocene to Early Pleistocene 9.0 1
## 21 Late Pleistocene 83.0 1
## 22 Late Pleistocene 95.0 1
## 23 Late Pleistocene 40.0 1
## 24 Late Pleistocene 58.0 35-40 1
## 25 Late Pleistocene 34.0 1
## 26 Late Pleistocene 29.0 1
## 27 Late Pleistocene 20.0 1
## 28 Late Pleistocene to Early Holocene 23.0 1
## 29 Late Pleistocene to Early Holocene 45.0 1
## 30 Late Pleistocene to Early Holocene? 60.0 1
## 31 Late Pleistocene to Late Holocene 50.0 35-40 1
## 32 Late Pliocene to Early Pleistocene 100.0 105-110 1
## 33 Late Pliocene to Early Pleistocene 190.0 18-26 1
## 34 Late Pliocene to Early Pleistocene 22.0 1
## 35 Late Pliocene to Early Pleistocene 100.0 1
## 36 Late Pliocene to Early Pleistocene 90.0 120-125 1
## 37 Late Pliocene to Early Pleistocene 195.0 1
## 38 Late Pliocene to Early Pleistocene 120.0 1
## 39 Late Pliocene to Early Pleistocene 20.0 1
## 40 Late Pliocene to Early Pleistocene 12.0 1
## 41 Late Pliocene to Early Pleistocene 22.5 1
## 42 Late Pliocene to Early Pleistocene 15.0 1
## 43 Late Pliocene to Early Pleistocene 186.0 1
## 44 Late Pliocene to EarlyPleistocene? 62.5 1
## 45 Middle Pleistocene 87.5 1
## 46 Middle Pleistocene 85.0 1
## 47 Middle Pleistocene 50.0 1
## 48 Middle Pleistocene 20.0 1
## 49 Middle Pleistocene 25.0 180-210 1
## 50 Middle Pleistocene 13.0 1
## 51 Middle Pleistocene 27.5 140-190 1
## 52 Middle Pleistocene 52.0 70-90 1
## 53 Middle Pleistocene to Early Holocene 122.5 100-140 1
## 54 Middle to Late Pleistocene 80.0 1
## 55 Middle to Late Pleistocene 37.5 1
## 56 Middle to Late Pleistocene 150.0 1
## 57 Modern 79.0 1
## 58 Modern 98.0 1
## 59 Modern 96.0 27-28 1
## 60 Modern 88.0 1
## 61 Modern 90.0 25-30 1
## 62 Modern 100.0 1
## 63 Modern 46.0 1
## 64 Modern 100.0 22-23 1
## 65 Modern 120.0 1
## 66 Modern 110.0 1
## 67 Pleistocene 40.0 1
## 68 Pleistocene 150.0 110-130 1
## 69 Pleistocene to Early Holocene 90.0 185-200 1
## 70 Pliocene to Early Pleistocene? 120.0 1
## 71 Tertiary; Pliocene to Early Pleistocene? 27.5 48-56 1The first plot shows mean Cl size for each taxon as a single data point, so each data point is one species (in this case this equals one individual, since I don’t have sample sizes), even within time bins.
Test1 <- test %>%
mutate(mm = CL_mean, vv=0, nn= n, tt=Age_mean) %>%
dplyr::select(mm, vv, nn, tt)
paleoTest1 <-as.paleoTS(Test1$mm, Test1$vv, Test1$nn, Test1$tt, MM = NULL,
genpars = NULL, label = "Testudinidae body size evolution mode")
paleoTest1## $mm
## [1] 107.5 24.0 28.0 23.0 165.0 80.0 120.0 175.0 11.0 90.0 33.0
## [12] 150.0 115.0 125.0 47.0 75.0 37.5 192.5 195.0 9.0 83.0 95.0
## [23] 40.0 58.0 34.0 29.0 20.0 23.0 45.0 60.0 50.0 100.0 190.0
## [34] 22.0 100.0 90.0 195.0 120.0 20.0 12.0 22.5 15.0 186.0 62.5
## [45] 87.5 85.0 50.0 20.0 25.0 13.0 27.5 52.0 122.5 80.0 37.5
## [56] 150.0 79.0 98.0 96.0 88.0 90.0 100.0 46.0 100.0 120.0 110.0
## [67] 40.0 150.0 90.0 120.0 27.5
##
## $vv
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [36] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [71] 0
##
## $nn
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [71] 1
##
## $tt
## [1] 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## [8] 0.00000 0.00000 0.00000 0.00000 0.00000 -1.28765 -1.28765
## [15] -1.28765 -1.28765 -1.28765 4.99550 3.23250 1.40950 -1.22465
## [22] -1.22465 -1.22465 -1.22465 -1.22465 -1.22465 -1.22465 -1.23000
## [29] -1.23000 -1.23000 -1.23050 0.56950 0.56950 0.56950 0.56950
## [36] 0.56950 0.56950 0.56950 0.56950 0.56950 0.56950 0.56950
## [43] 0.56950 0.56950 -0.84000 -0.84000 -0.84000 -0.84000 -0.84000
## [50] -0.84000 -0.84000 -0.84000 -0.89715 -0.89715 -0.89715 -0.89715
## [57] -1.29350 -1.29350 -1.29350 -1.29350 -1.29350 -1.29350 -1.29350
## [64] -1.29350 -1.29350 -1.29350 0.00635 0.00635 0.05050 1.00650
## [71] 1.00650
##
## $MM
## NULL
##
## $genpars
## NULL
##
## $label
## [1] "Testudinidae body size evolution mode"
##
## $start.age
## [1] 1.2935
##
## $timeDir
## [1] "increasing"
##
## attr(,"class")
## [1] "paleoTS"plot(paleoTest1)
This is the underlying data for Test1:
Test1## mm vv nn tt
## 1 107.5 0 1 1.29350
## 2 24.0 0 1 1.29350
## 3 28.0 0 1 1.29350
## 4 23.0 0 1 1.29350
## 5 165.0 0 1 1.29350
## 6 80.0 0 1 1.29350
## 7 120.0 0 1 1.29350
## 8 175.0 0 1 1.29350
## 9 11.0 0 1 1.29350
## 10 90.0 0 1 1.29350
## 11 33.0 0 1 1.29350
## 12 150.0 0 1 1.29350
## 13 115.0 0 1 0.00585
## 14 125.0 0 1 0.00585
## 15 47.0 0 1 0.00585
## 16 75.0 0 1 0.00585
## 17 37.5 0 1 0.00585
## 18 192.5 0 1 6.28900
## 19 195.0 0 1 4.52600
## 20 9.0 0 1 2.70300
## 21 83.0 0 1 0.06885
## 22 95.0 0 1 0.06885
## 23 40.0 0 1 0.06885
## 24 58.0 0 1 0.06885
## 25 34.0 0 1 0.06885
## 26 29.0 0 1 0.06885
## 27 20.0 0 1 0.06885
## 28 23.0 0 1 0.06350
## 29 45.0 0 1 0.06350
## 30 60.0 0 1 0.06350
## 31 50.0 0 1 0.06300
## 32 100.0 0 1 1.86300
## 33 190.0 0 1 1.86300
## 34 22.0 0 1 1.86300
## 35 100.0 0 1 1.86300
## 36 90.0 0 1 1.86300
## 37 195.0 0 1 1.86300
## 38 120.0 0 1 1.86300
## 39 20.0 0 1 1.86300
## 40 12.0 0 1 1.86300
## 41 22.5 0 1 1.86300
## 42 15.0 0 1 1.86300
## 43 186.0 0 1 1.86300
## 44 62.5 0 1 1.86300
## 45 87.5 0 1 0.45350
## 46 85.0 0 1 0.45350
## 47 50.0 0 1 0.45350
## 48 20.0 0 1 0.45350
## 49 25.0 0 1 0.45350
## 50 13.0 0 1 0.45350
## 51 27.5 0 1 0.45350
## 52 52.0 0 1 0.45350
## 53 122.5 0 1 0.39635
## 54 80.0 0 1 0.39635
## 55 37.5 0 1 0.39635
## 56 150.0 0 1 0.39635
## 57 79.0 0 1 0.00000
## 58 98.0 0 1 0.00000
## 59 96.0 0 1 0.00000
## 60 88.0 0 1 0.00000
## 61 90.0 0 1 0.00000
## 62 100.0 0 1 0.00000
## 63 46.0 0 1 0.00000
## 64 100.0 0 1 0.00000
## 65 120.0 0 1 0.00000
## 66 110.0 0 1 0.00000
## 67 40.0 0 1 1.29985
## 68 150.0 0 1 1.29985
## 69 90.0 0 1 1.34400
## 70 120.0 0 1 2.30000
## 71 27.5 0 1 2.30000For the second plot, I averaged CL means across taxa for each time bin, which leaves one data point per time bin, comprising all taxa within the respective bin:
Test2 <- test %>%
group_by(Age_mean) %>%
summarise(mm = mean(CL_mean), nn=n(), vv=var(CL_mean)) %>%
mutate(tt=Age_mean) %>%
dplyr::select(mm, vv, nn, tt)
# NA: column 2, rows 3, 10, 13, 14, 15
Test2[3,2] <- 0
Test2[10,2] <- 0
Test2[13,2] <- 0
Test2[14,2] <- 0
Test2[15,2] <- 0
paleoTest2 <-as.paleoTS(Test2$mm, Test2$vv, Test2$nn, Test2$tt, MM = NULL,
genpars = NULL, label = "Testudinidae body size evolution mode")
paleoTest2## $mm
## [1] 92.70000 79.90000 50.00000 42.66667 51.28571 97.50000 45.00000
## [8] 83.87500 95.00000 90.00000 87.30769 73.75000 9.00000 195.00000
## [15] 192.50000
##
## $vv
## [1] 398.6778 1542.5500 0.0000 346.3333 810.5714 2429.1667 833.6429
## [8] 3589.5511 6050.0000 0.0000 4816.0224 4278.1250 0.0000 0.0000
## [15] 0.0000
##
## $nn
## [1] 10 5 1 3 7 4 8 12 2 1 13 2 1 1 1
##
## $tt
## [1] 0.00000 0.00585 0.06300 0.06350 0.06885 0.39635 0.45350 1.29350
## [9] 1.29985 1.34400 1.86300 2.30000 2.70300 4.52600 6.28900
##
## $MM
## NULL
##
## $genpars
## NULL
##
## $label
## [1] "Testudinidae body size evolution mode"
##
## $start.age
## NULL
##
## $timeDir
## [1] "increasing"
##
## attr(,"class")
## [1] "paleoTS"plot(paleoTest2)
Since “real” variances and sample sizes are available when pooling all taxa, you can even fit models (as you should be able to in the end). (when I remember correctly, the model with the highest Akaike.wt is the best supported one, in this case this would be URW = random walk)
a=fit3models(paleoTest2, silent=FALSE, method="AD", pool=FALSE) #not working with Test1, because no variances/sample sizes available, I guess##
## Comparing 3 models [n = 14, method = AD]
##
## logL K AICc Akaike.wt
## GRW -70.40398 2 145.8989 0.373
## URW -71.26818 1 144.8697 0.625
## Stasis -75.70460 2 156.5001 0.002str(a)## 'data.frame': 3 obs. of 4 variables:
## $ logL : num -70.4 -71.3 -75.7
## $ K : num 2 1 2
## $ AICc : num 146 145 157
## $ Akaike.wt: num 0.373 0.625 0.002a$AICc[1] # not sure what this tells me...## [1] 145.8989This is the underlying data for Test2:
Test2## # A tibble: 15 × 4
## mm vv nn tt
## <dbl> <dbl> <int> <dbl>
## 1 92.70000 398.6778 10 0.00000
## 2 79.90000 1542.5500 5 0.00585
## 3 50.00000 0.0000 1 0.06300
## 4 42.66667 346.3333 3 0.06350
## 5 51.28571 810.5714 7 0.06885
## 6 97.50000 2429.1667 4 0.39635
## 7 45.00000 833.6429 8 0.45350
## 8 83.87500 3589.5511 12 1.29350
## 9 95.00000 6050.0000 2 1.29985
## 10 90.00000 0.0000 1 1.34400
## 11 87.30769 4816.0224 13 1.86300
## 12 73.75000 4278.1250 2 2.30000
## 13 9.00000 0.0000 1 2.70300
## 14 195.00000 0.0000 1 4.52600
## 15 192.50000 0.0000 1 6.28900TO DO:
06.06.2017
Try paleoTS with some first real data. Here is the underlying data:
tidyCL<-read.csv("tortoises_tidy.csv", sep=";", header=TRUE)
tidyCL## Country Latitude Longitude
## 1 USA 37.6000 -120.6000
## 2 USA 37.6000 -120.8000
## 3 USA 37.6000 -120.6000
## 4 USA 38.6665 -76.5298
## 5 USA 37.2242 -100.4176
## 6 USA 42.0000 -97.0000
## 7 USA 34.9000 -101.6000
## 8 USA 27.7000 -82.5000
## 9 USA 42.7000 -100.0000
## 10 USA 29.7000 -82.6000
## 11 USA 29.6000 -82.4000
## 12 Greece 40.4046 22.8980
## 13 Greece 40.4046 22.8980
## 14 Germany 47.8356 8.7490
## 15 Germany 47.8356 8.7490
## 16 Germany 47.8356 8.7490
## 17 Germany 47.8356 8.7490
## 18 Germany 47.8356 8.7490
## 19 Germany 47.8356 8.7490
## 20 Germany 47.8356 8.7490
## 21 Germany 47.8356 8.7490
## 22 Germany 47.8356 8.7490
## 23 Mongolia 47.1000 93.1667
## 24 Mongolia 47.1000 93.1667
## 25 USA 37.0000 -100.0000
## 26 USA 37.0000 -100.0000
## 27 France 44.8120 0.2133
## 28 France 43.6000 1.4333
## 29 Georgia 41.3200 44.3500
## 30 USA 35.4000 -76.8000
## 31 USA 35.3000 -118.5000
## 32 USA 35.3000 -118.5000
## 33 USA 35.3000 -118.5000
## 34 USA 29.7000 -82.6000
## 35 USA 29.7000 -82.6000
## 36 Colombia 3.2000 -75.2000
## Formation.Location.comment
## 1 Upper Mehrten Formation, Modesto Reservoir Member, Hemphillian
## 2 San Pablo Formation, Clarendonian
## 3 Upper Mehrten Formation, Modesto Reservoir Member, Hemphillian
## 4 Shattuck zones 10+11, Plum Point Member (Plum Point B), Calvert Formation
## 5 Rancholabrean
## 6 Late Hemphillian
## 7 poorly lithified, coarse-grained, brown sandstone
## 8 late early Irvingtonian
## 9 Blancan also known as: UNSM Sand Draw locality; Frick Prospecting Locality 277; Magill; Owl Pellet; UM-Neb. sites 1-66, 2-66, 2-67, 4-67, 5-67, 1-68
## 10 Alachua Formation
## 11 Illinoian or Kansan according to Lynch 1965
## 12 Gonia Formation
## 13 Gonia Formation
## 14 pedogenic gypsum, red and yellow-mottled clays and marls
## 15 pedogenic gypsum, red and yellow-mottled clays and marls
## 16 pedogenic gypsum, red and yellow-mottled clays and marls
## 17 pedogenic gypsum, red and yellow-mottled clays and marls
## 18 pedogenic gypsum, red and yellow-mottled clays and marls
## 19 pedogenic gypsum, red and yellow-mottled clays and marls
## 20 pedogenic gypsum, red and yellow-mottled clays and marls
## 21 pedogenic gypsum, red and yellow-mottled clays and marls
## 22 pedogenic gypsum, red and yellow-mottled clays and marls
## 23 160 km SW of Kobdo, Great Lake Area
## 24 160 km SW of Kobdo, Great Lake Area
## 25 XI Member of lower part of the Rexroad Formation
## 26 XI Member of lower part of the Rexroad Formation
## 27 Molasse inférieur du Fronsadai
## 28 Protaceratherium cf. minutum (Cuvier, 1822)
## 29 -
## 30 Yorktown Formation muddy sand
## 31 Dove Spring Formation, Cerrotejonian, Clarendonian (CL1, CL2)
## 32 Dove Spring Formation, Montediablan, Clarendonian (CL2, CL3)
## 33 Dove Spring Formation, Montediablan, Clarendonian (CL2, CL3)
## 34 a sinkhole lake that then collapsed into a larger underground chamber earliest Hemmingfordian North American Land Mammal Age (NALMA) only Holman 1965, not Holman 2003: Leptodactylus abavus Holman, J. A. 1965. Quarterly Journal of the Florida Academy of Sciences 28(1):70-72, fig. 1. (Anura: Leptodactylidae; early Miocene). Holotype: UF 10201. Rana bucella, Holman, J. A. 1965. Quarterly Journal of the Florida Academy of Sciences 28(1):76-77, fig. 2. (Anura: Ranidae; early Miocene). Holotype: UF/FGS 6071. Rana miocenica Holman, J. A. 1965. Quarterly Journal of the Florida Academy of Sciences 28(1):74-76, fig. 2. (Anura: Ranidae; early Miocene). Holotype: UF/FGS 6069.
## 35 a sinkhole lake that then collapsed into a larger underground chamber earliest Hemmingfordian North American Land Mammal Age (NALMA) only Holman 1965, not Holman 2003: Leptodactylus abavus Holman, J. A. 1965. Quarterly Journal of the Florida Academy of Sciences 28(1):70-72, fig. 1. (Anura: Leptodactylidae; early Miocene). Holotype: UF 10201. Rana bucella, Holman, J. A. 1965. Quarterly Journal of the Florida Academy of Sciences 28(1):76-77, fig. 2. (Anura: Ranidae; early Miocene). Holotype: UF/FGS 6071. Rana miocenica Holman, J. A. 1965. Quarterly Journal of the Florida Academy of Sciences 28(1):74-76, fig. 2. (Anura: Ranidae; early Miocene). Holotype: UF/FGS 6069.
## 36 Honda group, Cerbatana gravels and clays
## MAmin Mamax Genus Species Taxon
## 1 5.000 6.000 Hesperotestudo orthopygia Hesperotestudo orthopygia
## 2 9.000 10.000 Hesperotestudo sp. Hesperotestudo sp.
## 3 5.000 6.000 Hesperotestudo orthopygia Hesperotestudo orthopygia
## 4 15.000 15.800 Floridemys hurdi Floridemys hurdi
## 5 0.300 0.300 Hesperotestudo equicomes Hesperotestudo equicomes
## 6 4.800 5.200 Geochelone sp. Geochelone sp.
## 7 1.800 3.600 Gopherus canyonensis Gopherus canyonensis
## 8 1.000 1.500 Hesperotestudo crassiscutata Hesperotestudo crassiscutata
## 9 3.000 3.000 Hesperotestudo oelrichi Hesperotestudo oelrichi
## 10 10.900 11.000 Hesperotestudo alleni Hesperotestudo alleni
## 11 0.012 0.126 Hesperotestudo incisa Hesperotestudo incisa
## 12 2.600 5.300 Titanochelon bacharidisi Titanochelon bacharidisi
## 13 2.600 5.300 Titanochelon bacharidisi Titanochelon bacharidisi
## 14 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 15 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 16 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 17 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 18 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 19 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 20 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 21 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 22 13.000 13.000 Paleotestudo antiqua Paleotestudo antiqua
## 23 2.600 5.300 Ergilemys oskarkuhni Ergilemys oskarkuhni
## 24 2.600 5.300 Ergilemys oskarkuhni Ergilemys oskarkuhni
## 25 3.000 3.000 Hesperotestudo riggsi Hesperotestudo riggsi
## 26 3.000 3.000 Hesperotestudo riggsi Hesperotestudo riggsi
## 27 33.900 34.000 Cheirogaster maurini Cheirogaster maurini
## 28 23.030 23.200 Ergilemys bruneti Ergilemys bruneti
## 29 1.770 1.770 Testudo graeca Testudo graeca
## 30 4.000 5.000 Geochelone sp. Geochelone sp.
## 31 11.200 12.500 Gopherus ? sp. Gopherus ? sp.
## 32 9.000 11.200 Geochelone sp. Geochelone sp.
## 33 9.000 11.200 Gopherus ? sp. Gopherus ? sp.
## 34 18.000 19.000 Geochelone tedwhitei Geochelone tedwhitei
## 35 18.000 19.000 Geochelone tedwhitei Geochelone tedwhitei
## 36 6.000 11.000 Geochelone hesterna Geochelone hesterna
## CL PL
## 1 1200 NA
## 2 1200 NA
## 3 NA 620.0
## 4 NA NA
## 5 NA NA
## 6 NA 160.0
## 7 NA 805.0
## 8 NA 510.0
## 9 NA 258.0
## 10 NA 219.0
## 11 NA 211.6
## 12 1196 1150.0
## 13 1165 1120.0
## 14 185 NA
## 15 229 NA
## 16 220 NA
## 17 195 NA
## 18 206 NA
## 19 196 NA
## 20 NA 102.0
## 21 150 NA
## 22 145 NA
## 23 NA 180.0
## 24 220 NA
## 25 176 189.0
## 26 185 NA
## 27 400 NA
## 28 400 NA
## 29 195 NA
## 30 880 700.0
## 31 500 NA
## 32 500 NA
## 33 500 NA
## 34 370 NA
## 35 NA 400.0
## 36 278 NA
## verbal
## 1 <NA>
## 2 very large (comparable to specimens from Mehrten Formation)
## 3 <NA>
## 4 smaller than Hesperotestudo or Gopherus
## 5 medium to lage-sized Hesperotestudo, smaller than G. oelrichi
## 6 <NA>
## 7 <NA>
## 8 small (subgenus Hesperotestudo) lines
## 9 G. oelrichi is among the largest of the Geochelone (Hesperotestudo) turgida group
## 10 <NA>
## 11 <NA>
## 12 <NA>
## 13 subadult
## 14 <NA>
## 15 <NA>
## 16 <NA>
## 17 <NA>
## 18 <NA>
## 19 incomplete
## 20 <NA>
## 21 <NA>
## 22 <NA>
## 23 <NA>
## 24 <NA>
## 25 Holotype KUMVP 6789: CL: 176 mm(, C width: 155 mm, C height: 85 mm), PL: 189 mm(, P width: 140 mm)
## 26 Paratype KUMVP 6790: CL: 185 mm(, CW 165 mm)
## 27 close to 40 cm
## 28 <NA>
## 29 <NA>
## 30 large
## 31 Tortoises (Geochelone sp. and ?Gopherus sp. with carapaces up to 0.5 m in length are found throughout the section
## 32 Tortoises (Geochelone sp. and ?Gopherus sp. with carapaces up to 0.5 m in length are found throughout the section
## 33 Tortoises (Geochelone sp. and ?Gopherus sp. with carapaces up to 0.5 m in length are found throughout the section
## 34 CL: 370 mm, CW: 300 mm, CH approx. 150 mm
## 35 several specimens: not exceeding 400 mm in PL
## 36 <NA>
## estimated..e..from.verbal.description..ev..from.plastron..ep..or.measured..m..measured.from.figure..mf..E..extrapolated..
## 1 e
## 2 ev
## 3 e
## 4 ev
## 5 ev
## 6 e
## 7 m
## 8 m
## 9 m
## 10 m
## 11 m
## 12 mE
## 13 mE
## 14 mf
## 15 mf
## 16 mf
## 17 mf
## 18 mf
## 19 mf
## 20 mf
## 21 mf
## 22 mf
## 23 m
## 24 m
## 25 m
## 26 m
## 27 m
## 28 m
## 29 mf
## 30 m
## 31 m
## 32 m
## 33 m
## 34 m
## 35 m
## 36 mPrepare data for conversion to paleoTS-object:
TidyCL <- tidyCL %>%
select(MAmin, Mamax, CL) %>%
filter(CL != "NA") %>%
mutate(tt= (MAmin+Mamax)/2) %>% # create mean age
group_by(tt) %>% #create time bins
summarise(mm=mean(CL), vv=var(CL), nn=n()) #create means etc. for each time bin
TidyCL[is.na(TidyCL)]<-0 #subset NAs with O for
TidyCL## # A tibble: 13 × 4
## tt mm vv nn
## <dbl> <dbl> <dbl> <int>
## 1 1.770 195.0000 0.0000 1
## 2 3.000 180.5000 40.5000 2
## 3 3.950 860.3333 307760.3333 3
## 4 4.500 880.0000 0.0000 1
## 5 5.500 1200.0000 0.0000 1
## 6 8.500 278.0000 0.0000 1
## 7 9.500 1200.0000 0.0000 1
## 8 10.100 500.0000 0.0000 2
## 9 11.850 500.0000 0.0000 1
## 10 13.000 190.7500 911.9286 8
## 11 18.500 370.0000 0.0000 1
## 12 23.115 400.0000 0.0000 1
## 13 33.950 400.0000 0.0000 1bins <- tidyCL %>%
# select(MAmin, Mamax, CL) %>%
filter(CL != "NA") %>%
mutate(tt= (MAmin+Mamax)/2) %>% # create mean age
group_by(tt)
bins## Source: local data frame [24 x 14]
## Groups: tt [13]
##
## Country Latitude Longitude
## <fctr> <dbl> <dbl>
## 1 USA 37.6000 -120.600
## 2 USA 37.6000 -120.800
## 3 Greece 40.4046 22.898
## 4 Greece 40.4046 22.898
## 5 Germany 47.8356 8.749
## 6 Germany 47.8356 8.749
## 7 Germany 47.8356 8.749
## 8 Germany 47.8356 8.749
## 9 Germany 47.8356 8.749
## 10 Germany 47.8356 8.749
## # ... with 14 more rows, and 11 more variables:
## # Formation.Location.comment <fctr>, MAmin <dbl>, Mamax <dbl>,
## # Genus <fctr>, Species <fctr>, Taxon <fctr>, CL <int>, PL <dbl>,
## # verbal <fctr>,
## # estimated..e..from.verbal.description..ev..from.plastron..ep..or.measured..m..measured.from.figure..mf..E..extrapolated.. <fctr>,
## # tt <dbl>library(paleoTS)
paleoTidyCL <-as.paleoTS(TidyCL$mm, TidyCL$vv, TidyCL$nn, TidyCL$tt, MM = NULL, genpars = NULL, label = "Testudinidae body size evolution mode")
paleoTidyCL## $mm
## [1] 195.0000 180.5000 860.3333 880.0000 1200.0000 278.0000 1200.0000
## [8] 500.0000 500.0000 190.7500 370.0000 400.0000 400.0000
##
## $vv
## [1] 0.0000 40.5000 307760.3333 0.0000 0.0000
## [6] 0.0000 0.0000 0.0000 0.0000 911.9286
## [11] 0.0000 0.0000 0.0000
##
## $nn
## [1] 1 2 3 1 1 1 1 2 1 8 1 1 1
##
## $tt
## [1] 0.000 1.230 2.180 2.730 3.730 6.730 7.730 8.330 10.080 11.230
## [11] 16.730 21.345 32.180
##
## $MM
## NULL
##
## $genpars
## NULL
##
## $label
## [1] "Testudinidae body size evolution mode"
##
## $start.age
## [1] 1.77
##
## $timeDir
## [1] "increasing"
##
## attr(,"class")
## [1] "paleoTS"plot(paleoTidyCL)
fit3models(paleoTidyCL, silent=FALSE, method="AD", pool=FALSE) #not working with Test1, because no variances/sample sizes available, I guess##
## Comparing 3 models [n = 12, method = AD]
##
## logL K AICc Akaike.wt
## GRW -94.17833 2 193.6900 0.001
## URW -104.38851 1 211.1770 0.000
## Stasis -87.43929 2 180.2119 0.999Map <- tidyCL %>%
select(Genus, Taxon, Latitude, Longitude, Country, CL, PL) %>%
group_by(Latitude) %>%
mutate(count= n())
mapWorld <- borders("world", colour="azure3", fill="azure3") # create a layer of borders
mp <- Map %>%
ggplot(aes(Longitude, Latitude)) + mapWorld +
# geom_point(fill="red", colour="red", size=0.5) +
geom_point(aes(Longitude, Latitude,colour=CL, size=count))
mp
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(mp)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`TO DO:
- map localities with differing colors for: CL available, CL extrapolated (from PL or figures), CL missing
- complete data set!
- get missing refences/make list of missing references
08.06.17
Map all localities with sample size and age indicated (regardless of whether CL information is available):
test<-read.csv("tortoises13-04.csv", sep=";", header=TRUE)
colnames(test)[6] <- "Mamin"
colnames(test)[7] <- "Mamax"
Test <- test %>%
select(Locality, Country, Latitude, Longitude, Mamin, Mamax, Epoch, Genus, Species, Taxon, CL) %>%
mutate(Age= (Mamin+Mamax)/2) %>% # create mean age
group_by(Latitude) %>%
mutate(count= n())
mapWorld <- borders("world", colour="azure3", fill="azure3") # create a layer of borders
map <- Test %>%
ggplot(aes(Longitude, Latitude)) + mapWorld +
#geom_point(fill="red", colour="red", size=0.5) +
geom_point(aes(Longitude, Latitude,colour=Age, size=count))
map
ggplotly(map)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.
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